The Comparison Between Genetic Simulated Annealing Algorithm and Ant Colony Optimization Algorithm for ASP

Abstract

Assembly sequence planning plays an important role in the product development process. It is an important factor that determines quality and cost of the product assembly. Cost in assembly can be reduced by the implementation of generating automatic product assembly sequences, and selecting the optimum sequence in product assembly process. Assembly sequence planning (ASP) is combinatorial problem. In recent years, some soft computing & intelligent algorithms have been used to solve ASP problems, and some achievements are arrived at. However, there are limitations for ASP. GA heavily depends on the choosing original sequence, which can result in early convergence in iterative operation, lower searching efficiency in evolutionary process, and non-optimization of final result for global variable. For simulated annealing algorithms, the principle of generating new sequence is exchanging position of the randomly selected two parts. Obviously, for complex products, a number of non-feasible solutions may appear, and the efficiency is low. In view of these limitations, the approaches of genetic simulated annealing algorithm (GSAA), ant colony optimization (ACO) algorithm and so on are used for the optimization of ASP. In this paper, the following contents about these two algorithms and the comparison are included. Firstly, the relevant researches on assembly sequence planning and the application of GA and SA are summarized. Next, the idea of two algorithms into genetic simulated annealing algorithm and ant colony optimization algorithm are put forward individually. Thirdly, a case study is presented to validate the proposed two methods. The advantages and disadvantages are presented. At last, the work of this paper is summarized and the future works are given.